In this paper, a novel simultaneous wireless power and data transfer (SWPDT) system is proposed for underwater wireless sensor networks. A pair of coils is used to establish forward power transfer and bidirectional data transfer links based on coupled magnetic resonance. While transmitting power to the load, the forward power waves are also used as the carrier for data transfer, thereby avoiding the use of an additional carrier source on the power transmitter-side. An improved double-LCC double-CLC topology is presented to achieve both impedance matching and wave filtering of the SWPDT system in full-duplex mode, instead of using additional wave trappers, transformers or tap coils. Compared with previous works, the proposed system can simultaneously achieve high power gains, low mutual interference and simple circuit structure. Moreover, to deal with the conflicts between the power gains and the bandwidths of the channels, an improved multi-objective optimization method is proposed for the SWPDT system. Using this method, the Pareto optimal solutions of the system parameters can be found and the comprehensive performance of the SWPDT system can be further improved. Finally, an experimental prototype is established, which achieves full-duplex wireless power and data transfer, with a maximum load power of 23.1W, a forward data transfer rate of 20kbps and a backward data transfer of 300kbps. There is almost no interference between power and data transfer. The experimental results show that the proposed SWPDT system has good application prospects in underwater sensor node charging and full-duplex communication.

This article proposes a single-transmitter multi-receiver wireless power transfer (STMR-WPT) system, which uses a cross-overlapped bipolar coil as the transmitter and multiple square unipolar coils as the receivers. By using this structure, the magnetic field of the system can be adjusted to accommodate different coil misalignment conditions. In addition, the proposed system uses C-CLCs networks to achieve separate load power allocation. Thus, relay coils, complex multi-frequency transmission channels and multiple independent power supplies can be avoided. A mapping impedance-based circuit model was established to analyze the characteristics of the system, and then a single-frequency power allocation method was presented. Through this method, the STMR-WPT system can achieve load power allocation at any specified ratios under different mutual inductance and load impedance conditions. Finally, an experimental STMR-WPT system was built. The side lengths of the transmitter and receiver coils are 400 mm and 160 mm, respectively. The measurement results indicated that when the lateral or longitudinal coil misalignment varies within the range of 0~200 mm, the coupling coefficient decreases by a maximum of 6% compared to the initial value, and when the angular coil misalignment varies within the range of 0~90 degrees, the coupling coefficient decreases by a maximum of 22% compared to the initial value. In four different power allocation scenarios, the experimental STMR-WPT system successfully achieved the expected power allocation goals.

Magnetically-coupled resonant wireless power transfer (MCR-WPT) technology is one of the most promising technologies to power embedded sensors. In many practical situations, embedded sensors are located in a metal environment. The nearby metal materials will affect the performance of the MCR-WPT system. In this paper, the equivalent circuit model of the MCR-WPT system in metal environment is established and the factors affecting the transmission efficiency is analyzed. Then, simulation analysis is performed using Maxwell software and Simplorer software. Two cases are considered in the simulation study: the coil is close to a metal plate and the coil is close to a metal shaft. Simulation results show that metal materials will change the coil inductance and interfere with the magnetic field distribution of the MCR-WPT system, which in turn affects the transmission efficiency of the system.

The multi-transmitter single-receiver wireless power transfer (MTSR-WPT) system has good tolerance for coil misalignment because the magnetic fields generated by multiple transmitters can be shaped to adapt to position changes in the receiver coil. In order to achieve magnetic field shaping of the MTSR-WPT system and increase power transfer efficiency, accurately estimating the position of the receiver coil is a key issue that needs to be addressed. In this article, a receiver position estimation method based on long short-term memory (LSTM) is proposed, which utilizes a data-driven approach to establish a neural network model. By learning the relationship between the measured time-series voltage data of the transmitter coils and the position of the receiver coil, the proposed model can achieve accurate position estimation of the receiver. Compared with previous works, the proposed method does not require communication between the transmitter and receiver, which is conducive to simplifying the system structure and reducing costs. In addition, the proposed LSTM-based method requires less derivation of complex formulas and the internal mechanism analysis of the system. Finally, a MTSR-WPT prototype is built to verify the proposed method. The experimental results show that the proposed LSTM-based method can achieve high-accuracy position estimation of the receiver. When the receiver moves within a range of 160 mm × 160 mm, the average error between the estimated receiver coil position using the proposed method and the actual receiver coil position is less than 2.40 mm.

In this paper, an impedance matching method for the wireless power transfer system with multiple transmitting coils is proposed. Firstly, the equivalent circuit model of the wireless power transfer system is established, and the expressions of the loop currents and the system power gain are derived. Then, in order to improve the system performance, an impedance matching topology is presented. The parameters of the impedance matching topology are optimized by the particle swarm optimization algorithm. With this method, the power gain of the system can maintain high when the coupling coefficient changes at a certain range. Finally, the feasibility of the proposed method is verified through simulations.

Maintaining high power delivery under uncertain landing positions is a key challenge for wireless charging of unmanned aerial vehicles (UAVs). This paper presents a data-driven power optimization method based on the Soft Actor-Critic algorithm for multi-transmitter single-receiver wireless power transfer (MTSR-WPT) systems. To support effective learning without explicit online parameter identification, a physics-informed dual-current state representation is constructed from measurable current responses, combining a zero-phase current with the current response under the applied phase command. The agent is trained using a reward defined directly from normalized load power, and the transmitter voltage phases serve as the control actions. In simulations of a five-transmitter system, the learned policy achieves about 97% of the theoretical maximum power in the training region and about 96% in the expanded evaluation region. Additional robustness studies show strong performance under moderate measurement noise and substantial recovery under model mismatch after short fine-tuning. Experimental validation on a physical prototype confirms the effectiveness of the method, yielding an average power improvement of 188% from a zero-phase baseline and reaching 87% of the maximum power measured on the hardware platform. These results support the proposed method as a practical data-driven alternative to model-dependent MTSR-WPT power optimization for UAV wireless charging.